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We study few-shot learning in natural language domains.
Learning from one example through shared densities on transforms
Erik G Miller, Nicholas E Matsakis, and Paul A Viola. 2000 · 2000
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On spectral clustering: Analysis and an algorithm
Andrew Y Ng, Michael I Jordan, and Yair Weiss. 2002 · 2002
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One-shot learning of object categories
Fei-Fei Li, Rob Fergus, and Pietro Perona. 2006 · 2006
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
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Rank-sparsity incoherence for matrix decomposition
Venkat Chandrasekaran, Sujay Sanghavi, Pablo A Parrilo, and Alan S Willsky. 2011 · 2011
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Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
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Learning with whom to share in multi-task feature learning
Zhuoliang Kang, Kristen Grauman, and Fei Sha. 2011 · 2011
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One shot learning of simple visual concepts
Brenden M Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B Tenenbaum. 2011 · 2011
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Learning multiple tasks using shared hypotheses
Koby Crammer and Yishay Mansour. 2012 · 2012
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Learning task grouping and overlap in multi-task learning
Abhishek Kumar and Hal Daume III. 2012 · 2012
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Multimedia lego: Learning structured model by probabilistic logic ontology tree
Shiyu Chang, Guo-Jun Qi, Jinhui Tang, Qi Tian, Yong Rui, and Thomas S Huang. 2013 · 2013
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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Convex multi-task learning by clustering
Aviad Barzilai and Koby Crammer. 2015 · 2015
Cited alongside, same era.
Siamese neural networks for one-shot image recognition
Gregory Koch. 2015 · 2015
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum. 2015 · 2015
Cited alongside, same era.
Few-shot learning with meta metric learners
Yu Cheng, Mo Yu, Xiaoxiao Guo, and Bowen Zhou. 2017 · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel. 2017 · 2017
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Tsendsuren Munkhdalai and Hong Yu. 2017 · 2017
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Co-clustering for multitask learning
Keerthiram Murugesan, Jaime Carbonell, and Yiming Yang. 2017 · 2017
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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016 · 2016
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Supervised and semi-supervised text categorization using one-hot lstm for region embeddings
Rie Johnson and Tong Zhang. 2016 · 2016
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Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2016 · 2016
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al. 2016 · 2016
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle. 2017 · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S Zemel. 2017 · 2017
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Few-shot learning through an information retrieval lens
Eleni Triantafillou, Richard Zemel, and Raquel Urtasun. 2017 · 2017
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Learning to model the tail
Yu-Xiong Wang, Deva Ramanan, and Martial Hebert. 2017 · 2017
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The power of convex relaxation: Near-optimal matrix completion
Emmanuel J Candès and Terence Tao. 2010 · 2080
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